Abstract: A vehicle safety system performs close-in incipient collision detection that combines high sample rate near-field sensors with advanced real-time processing to accurately determine imminent threats and the likelihood of a collision before a collision occurs. This allows applicable countermeasures to be deployed based upon the probability of a collision, while also taking into account the type of threat and the impending impact's location on the vehicle. This radically new approach will soon transform the passive safety market to greatly reduce injuries from automotive crashes and save lives.

Abstract: To reliably detect an object such as a bicycle at increased range, an Advanced Driving Support System uses a deep neural network(s) to process an ambient (grey-scale) image into an object that is then tracked by a second range detection camera. Most objects of interest, such as bicycles and automobiles, are outfitted with one or more retroreflectors that are used to cue the neural network to the object of most interest. As the retroreflectors also tend to saturate the range detection camera, a method is used to manage the saturation and estimate the correct range to the object.

Abstract: A vehicle safety system performs close-in incipient collision detection that combines high sample rate near-field sensors with advanced real-time processing to accurately determine and report risky driver behavior.

Abstract: A vehicle safety system performs close-in incipient collision detection that combines high sample rate near-field sensors with advanced real-time processing to accurately determine imminent threats and the likelihood of a collision before a collision occurs. This allows applicable countermeasures to be deployed based upon the probability of a collision, while also taking into account the type of threat and the impending impact's location on the vehicle. This radically new approach will soon transform the passive safety market to greatly reduce injuries from automotive crashes and save lives.